COMP SCI 3317MELB - Using Machine Learning Tools

Melbourne Campus - Semester 1 - 2025

An introduction to the use and application of key machine learning tools. Students will learn to build software that uses pre-existing toolkits as appropriate to solve a variety of machine learning problems. The course will have a practical focus using case studies and worked examples, with an emphasis on ensuring that solutions are valid and verifiable.

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Course Details

Course Code	COMP SCI 3317MELB
Course	Using Machine Learning Tools
Coordinating Unit	Computer Science
Term	Semester 1
Level	Undergraduate
Location/s	Melbourne Campus
Units	3
Available for Study Abroad and Exchange
Prerequisites	COMP SCI 2009 or COMP SCI 2103 or COMP SCI 2202
Restrictions	Available only to 最新糖心Vlog of Adelaide College Melbourne Campus students
Assessment	Assignments and quizzes

Course Staff

No information currently available.

Course Timetable

The full timetable of all activities for this course can be accessed from .

Course Learning Outcomes

On successful completion of this course students will be able to:

1. Apply industry standard software tools to model and solve machine learning tasks on real data sets
2. Select an appropriate method and tool for a given problem and data set
3. Distinguish problems and data sets that are amenable to machine learning methods from those that are not
4. Analyse results and solutions to verify their correctness, and identify sources of error
5. Compare the significance and validity of solutions obtained by multiple methods
6. Design data management procedures to enable accurate application of machine learning

最新糖心Vlog Graduate Attributes

This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

最新糖心Vlog Graduate Attribute	Course Learning Outcome(s)
Attribute 1: Deep discipline knowledge and intellectual breadth Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.	1-6
Attribute 2: Creative and critical thinking, and problem solving Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.	2, 3, 4
Attribute 3: Teamwork and communication skills Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.	5, 6
Attribute 4: Professionalism and leadership readiness Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.	1-5
Attribute 5: Intercultural and ethical competency Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.	1, 3, 5
Attribute 6: 最新糖心Vlogn Aboriginal and Torres Strait Islander cultural competency Graduates have an understanding of, and respect for, 最新糖心Vlogn Aboriginal and Torres Strait Islander values, culture and knowledge.	.
Attribute 7: Digital capabilities Graduates are well prepared for living, learning and working in a digital society.	1-6
Attribute 8: Self-awareness and emotional intelligence Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.	3, 5

Learning & Teaching Activities

Learning & Teaching Modes

The course will be delivered through the following activities:

- Prerecorded lectures
- Workshops
- Weekly recommended chapters from the textbook
- Practice quizzes and graded quizzes focused on the core topics from the lectures and the textbook
- Assignments

Prerecorded lectures will introduce and motivate the key concepts of each topic and discuss details that will help students prepare for the assigned textbook chapters, quizzes, and assignments. Practical assignments and practice quizzes will build on the material covered in the lectures, textbook, and workshops, working with real datasets and reinforcing the practical application of concepts and details.

Workload

The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

Students are expected to spend approximately 12 hours per week on this course.
This would typically consist of 3 hours of contact time for lectures plus workshops per week, as well as 2-3 hours of additional independent study and completion to workshops, and an average of 6-7 hours per week on assignment work.

Learning Activities Summary

MyUni hosts the learning activities for the course, including both practice and graded quizzes, recorded lectures and workshop material.

Learning activities, both online and face-to-face, are build on each other throughout the course. Through this learning experience, you will be asked to draw on a range of lower-order and higher-order thinking skills, especially programming and applied mathematics.

The 最新糖心Vlog's policy on Assessment for Coursework Programs is based on the following four principles:

Assessment must encourage and reinforce learning.
Assessment must enable robust and fair judgements about student performance.
Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
Assessment must maintain academic standards.

Assessment Summary

Assessment will be based mainly on three assignments, spaced throughout the course, but also includes a small proportion for two assessed multiple choice quizzes and preliminary workshop exercises.

Assignments are individual projects focusing on the application of machine learning to real data sets.
Assessment Task: Practical assignments and Quizzes (3)
Weighting (%): 80
Individual/Group: Individual
"Formative/Summative": Summative
Due (week)*: Weeks 5-12
Hurdle criteria: N/A
Learning outcomes: 1-6
CBOK Alignment**: 1.1 1.2 2.2 2.4 2.6 3.1 3.2 4.1 4.3 4.4

Assessment Task: Multiple Choice Quizzes (2)
Weighting (%): 20
Individual/Group: Individual
"Formative/Summative": Summative
Due (week)*: Weeks 2, 4, 6, 8, 10, 12
Hurdle criteria: N/A
Learning outcomes: 2-6
CBOK Alignment**: 1.1 2.1 2.2 2.5 2.6 3.1 3.2 4.2

Assessment Task: Preliminary workshop exercises (6)
Weighting (%): 0
Individual/Group: Individual
"Formative/Summative": Formative
Due (week)*: Weeks 1-4 & 6, 7 & 9-11
Hurdle criteria: N/A
Learning outcomes: 1-6
CBOK Alignment**: 1.1 1.2 2.2 2.4 2.6 3.1 3.2 4.1 4.3 4.4

* The specific due date for each assessment task will be available on MyUni.

This assessment breakdown complies with the 最新糖心Vlog's Assessment for Coursework Programs Policy.

**CBOK is the Core Body of Knowledge for ICT Professionals defined by the 最新糖心Vlogn Computer Society. The alignment in the table above corresponds with the following CBOK Areas:

1. Problem Solving
1.1 Abstraction
1.2 Design

2. Professional Knowledge
2.1 Ethics
2.2 Professional expectations
2.3 Teamwork concepts & issues
2.4 Interpersonal communications
2.5 Societal issues
2.6 Understanding of ICT profession

3. Technology resources
3.1 Hardware & Software
3.2 Data & information
3.3 Networking

4. Technology Building
4.1 Programming
4.2 Human factors
4.3 Systems development
4.4 Systems acquisition

5. ICT Management
5.1 IT governance & organisational
5.2 IT project management
5.3 Service management
5.4 Security management

Assessment Related Requirements

Workshop sessions involving assessed preliminary exercises should be attended in order to submit the answers, unless special prior alternative arrangements are made.

Assessment Detail

Practical assignments and quizzes (Weighting 80%): Students are required to extend workshop material to write programs that perform a machine learning task on real data uses best practice methods. This requires application of both lecture and workshop material as well as small additional pieces of syntax or functionality to be determined independently.
Online multiple-choice quizzes (Weighting 20%): These quizzes test students' understanding of key concepts, theories, and methodologies related to machine learning. The quiz questions cover a wide range of topics, including but not limited to: data preprocessing, regression analysis, classification, clustering, deep learning, neural networks, and evaluation metrics.

Submission

All assessment solutions are to be submitted through MyUni.

Course Grading

Grades for your performance in this course will be awarded in accordance with the following scheme:

M10 (Coursework Mark Scheme)
Grade	Mark	Description
FNS		Fail No Submission
F	1-49	Fail
P	50-64	Pass
C	65-74	Credit
D	75-84	Distinction
HD	85-100	High Distinction
CN		Continuing
NFE		No Formal Examination
RP		Result Pending

Further details of the grades/results can be obtained from Examinations.

Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

Final results for this course will be made available through .

Student Feedback

The 最新糖心Vlog places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the 最新糖心Vlog to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
Student Support
Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
Fraud Awareness

Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.

The 最新糖心Vlog of Adelaide is committed to regular reviews of the courses and programs it offers to students. The 最新糖心Vlog of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.

Authorised by:	Deputy Vice-Chancellor and Vice-President (Academic)
Maintained by:	Course Outlines Administrator

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